680 lines
27 KiB
Python
680 lines
27 KiB
Python
"""
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Translation Service Abstraction
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Provides a unified interface for different translation providers
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Optimized for high performance with parallel processing and caching
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"""
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from abc import ABC, abstractmethod
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from typing import Optional, List, Dict, Tuple
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import requests
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from deep_translator import GoogleTranslator, DeeplTranslator, LibreTranslator
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from config import config
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import concurrent.futures
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import threading
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import asyncio
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from functools import lru_cache
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import time
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import hashlib
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from collections import OrderedDict
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# Global thread pool for parallel translations
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_executor = concurrent.futures.ThreadPoolExecutor(max_workers=8)
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class TranslationCache:
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"""Thread-safe LRU cache for translations to avoid redundant API calls"""
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def __init__(self, maxsize: int = 5000):
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self.cache: OrderedDict = OrderedDict()
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self.maxsize = maxsize
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self.lock = threading.RLock()
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self.hits = 0
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self.misses = 0
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def _make_key(self, text: str, target_language: str, source_language: str, provider: str) -> str:
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"""Create a unique cache key"""
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content = f"{provider}:{source_language}:{target_language}:{text}"
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return hashlib.md5(content.encode('utf-8')).hexdigest()
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def get(self, text: str, target_language: str, source_language: str, provider: str) -> Optional[str]:
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"""Get a cached translation if available"""
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key = self._make_key(text, target_language, source_language, provider)
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with self.lock:
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if key in self.cache:
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self.hits += 1
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# Move to end (most recently used)
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self.cache.move_to_end(key)
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return self.cache[key]
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self.misses += 1
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return None
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def set(self, text: str, target_language: str, source_language: str, provider: str, translation: str):
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"""Cache a translation result"""
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key = self._make_key(text, target_language, source_language, provider)
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with self.lock:
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if key in self.cache:
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self.cache.move_to_end(key)
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self.cache[key] = translation
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# Remove oldest if exceeding maxsize
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while len(self.cache) > self.maxsize:
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self.cache.popitem(last=False)
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def clear(self):
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"""Clear the cache"""
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with self.lock:
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self.cache.clear()
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self.hits = 0
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self.misses = 0
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def stats(self) -> Dict:
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"""Get cache statistics"""
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with self.lock:
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total = self.hits + self.misses
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hit_rate = (self.hits / total * 100) if total > 0 else 0
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return {
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"size": len(self.cache),
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"maxsize": self.maxsize,
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"hits": self.hits,
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"misses": self.misses,
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"hit_rate": f"{hit_rate:.1f}%"
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}
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# Global translation cache
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_translation_cache = TranslationCache(maxsize=5000)
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class TranslationProvider(ABC):
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"""Abstract base class for translation providers"""
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@abstractmethod
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def translate(self, text: str, target_language: str, source_language: str = 'auto') -> str:
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"""Translate text from source to target language"""
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pass
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def translate_batch(self, texts: List[str], target_language: str, source_language: str = 'auto') -> List[str]:
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"""Translate multiple texts at once - default implementation"""
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return [self.translate(text, target_language, source_language) for text in texts]
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def translate_batch_parallel(self, texts: List[str], target_language: str, source_language: str = 'auto', max_workers: int = 4) -> List[str]:
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"""Parallel batch translation using thread pool"""
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if not texts:
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return []
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results = [''] * len(texts)
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non_empty = [(i, t) for i, t in enumerate(texts) if t and t.strip()]
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if not non_empty:
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return [t if t else '' for t in texts]
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def translate_one(item: Tuple[int, str]) -> Tuple[int, str]:
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idx, text = item
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try:
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return (idx, self.translate(text, target_language, source_language))
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except Exception as e:
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print(f"Translation error at index {idx}: {e}")
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return (idx, text)
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with concurrent.futures.ThreadPoolExecutor(max_workers=max_workers) as executor:
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for idx, translated in executor.map(translate_one, non_empty):
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results[idx] = translated
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# Fill empty positions
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for i, text in enumerate(texts):
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if not text or not text.strip():
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results[i] = text if text else ''
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return results
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class GoogleTranslationProvider(TranslationProvider):
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"""Google Translate implementation with batch support and caching"""
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def __init__(self):
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self._local = threading.local()
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self.provider_name = "google"
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def _get_translator(self, source_language: str, target_language: str) -> GoogleTranslator:
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"""Get or create a translator instance for the current thread"""
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key = f"{source_language}_{target_language}"
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if not hasattr(self._local, 'translators'):
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self._local.translators = {}
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if key not in self._local.translators:
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self._local.translators[key] = GoogleTranslator(source=source_language, target=target_language)
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return self._local.translators[key]
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def translate(self, text: str, target_language: str, source_language: str = 'auto') -> str:
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if not text or not text.strip():
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return text
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# Check cache first
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cached = _translation_cache.get(text, target_language, source_language, self.provider_name)
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if cached is not None:
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return cached
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try:
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translator = self._get_translator(source_language, target_language)
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result = translator.translate(text)
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# Cache the result
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_translation_cache.set(text, target_language, source_language, self.provider_name, result)
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return result
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except Exception as e:
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print(f"Translation error: {e}")
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return text
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def translate_batch(self, texts: List[str], target_language: str, source_language: str = 'auto', batch_size: int = 50) -> List[str]:
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"""
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Translate multiple texts using batch processing for speed.
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Uses caching to avoid redundant translations.
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"""
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if not texts:
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return []
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# Filter and track empty texts
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results = [''] * len(texts)
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non_empty_indices = []
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non_empty_texts = []
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texts_to_translate = []
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indices_to_translate = []
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for i, text in enumerate(texts):
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if text and text.strip():
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# Check cache first
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cached = _translation_cache.get(text, target_language, source_language, self.provider_name)
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if cached is not None:
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results[i] = cached
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else:
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non_empty_indices.append(i)
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non_empty_texts.append(text)
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texts_to_translate.append(text)
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indices_to_translate.append(i)
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else:
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results[i] = text if text else ''
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if not texts_to_translate:
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return results
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try:
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translator = GoogleTranslator(source=source_language, target=target_language)
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# Process in batches
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translated_texts = []
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for i in range(0, len(texts_to_translate), batch_size):
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batch = texts_to_translate[i:i + batch_size]
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try:
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# Use translate_batch if available
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if hasattr(translator, 'translate_batch'):
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batch_result = translator.translate_batch(batch)
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else:
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# Fallback: join with separator, translate, split
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separator = "\n|||SPLIT|||\n"
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combined = separator.join(batch)
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translated_combined = translator.translate(combined)
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if translated_combined:
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batch_result = translated_combined.split("|||SPLIT|||")
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# Clean up results
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batch_result = [t.strip() for t in batch_result]
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# If split didn't work correctly, fall back to individual
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if len(batch_result) != len(batch):
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batch_result = [translator.translate(t) for t in batch]
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else:
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batch_result = batch
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translated_texts.extend(batch_result)
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except Exception as e:
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print(f"Batch translation error, falling back to individual: {e}")
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for text in batch:
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try:
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translated_texts.append(translator.translate(text))
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except:
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translated_texts.append(text)
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# Map back to original positions and cache results
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for idx, (original, translated) in zip(indices_to_translate, zip(texts_to_translate, translated_texts)):
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result = translated if translated else texts[idx]
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results[idx] = result
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# Cache successful translations
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_translation_cache.set(texts[idx], target_language, source_language, self.provider_name, result)
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return results
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except Exception as e:
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print(f"Batch translation failed: {e}")
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# Fallback to individual translations
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for idx, text in zip(indices_to_translate, texts_to_translate):
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try:
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results[idx] = GoogleTranslator(source=source_language, target=target_language).translate(text) or text
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except:
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results[idx] = text
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return results
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class DeepLTranslationProvider(TranslationProvider):
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"""DeepL Translate implementation with batch support"""
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def __init__(self, api_key: str):
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self.api_key = api_key
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self._translator_cache = {}
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def _get_translator(self, source_language: str, target_language: str) -> DeeplTranslator:
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key = f"{source_language}_{target_language}"
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if key not in self._translator_cache:
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self._translator_cache[key] = DeeplTranslator(api_key=self.api_key, source=source_language, target=target_language)
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return self._translator_cache[key]
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def translate(self, text: str, target_language: str, source_language: str = 'auto') -> str:
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if not text or not text.strip():
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return text
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try:
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translator = self._get_translator(source_language, target_language)
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return translator.translate(text)
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except Exception as e:
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print(f"Translation error: {e}")
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return text
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def translate_batch(self, texts: List[str], target_language: str, source_language: str = 'auto') -> List[str]:
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"""Batch translate using DeepL"""
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if not texts:
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return []
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results = [''] * len(texts)
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non_empty = [(i, t) for i, t in enumerate(texts) if t and t.strip()]
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if not non_empty:
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return [t if t else '' for t in texts]
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try:
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translator = self._get_translator(source_language, target_language)
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non_empty_texts = [t for _, t in non_empty]
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if hasattr(translator, 'translate_batch'):
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translated = translator.translate_batch(non_empty_texts)
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else:
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translated = [translator.translate(t) for t in non_empty_texts]
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for (idx, _), trans in zip(non_empty, translated):
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results[idx] = trans if trans else texts[idx]
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# Fill empty positions
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for i, text in enumerate(texts):
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if not text or not text.strip():
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results[i] = text if text else ''
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return results
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except Exception as e:
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print(f"DeepL batch error: {e}")
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return [self.translate(t, target_language, source_language) for t in texts]
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class LibreTranslationProvider(TranslationProvider):
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"""LibreTranslate implementation with batch support"""
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def __init__(self, custom_url: str = "https://libretranslate.com"):
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self.custom_url = custom_url
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self._translator_cache = {}
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def _get_translator(self, source_language: str, target_language: str) -> LibreTranslator:
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key = f"{source_language}_{target_language}"
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if key not in self._translator_cache:
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self._translator_cache[key] = LibreTranslator(source=source_language, target=target_language, custom_url=self.custom_url)
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return self._translator_cache[key]
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def translate(self, text: str, target_language: str, source_language: str = 'auto') -> str:
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if not text or not text.strip():
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return text
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try:
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translator = self._get_translator(source_language, target_language)
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return translator.translate(text)
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except Exception as e:
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print(f"LibreTranslate error: {e}")
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return text
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def translate_batch(self, texts: List[str], target_language: str, source_language: str = 'auto') -> List[str]:
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"""Batch translate using LibreTranslate"""
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if not texts:
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return []
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results = [''] * len(texts)
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non_empty = [(i, t) for i, t in enumerate(texts) if t and t.strip()]
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if not non_empty:
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return [t if t else '' for t in texts]
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try:
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translator = self._get_translator(source_language, target_language)
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for idx, text in non_empty:
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try:
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results[idx] = translator.translate(text) or text
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except:
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results[idx] = text
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for i, text in enumerate(texts):
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if not text or not text.strip():
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results[i] = text if text else ''
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return results
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except Exception as e:
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print(f"LibreTranslate batch error: {e}")
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return texts
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class OllamaTranslationProvider(TranslationProvider):
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"""Ollama LLM translation implementation"""
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def __init__(self, base_url: str = "http://localhost:11434", model: str = "llama3", vision_model: str = "llava", system_prompt: str = ""):
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self.base_url = base_url.rstrip('/')
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self.model = model.strip() # Remove any leading/trailing whitespace
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self.vision_model = vision_model.strip()
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self.custom_system_prompt = system_prompt # Custom context, glossary, instructions
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def translate(self, text: str, target_language: str, source_language: str = 'auto') -> str:
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if not text or not text.strip():
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return text
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# Skip very short text or numbers only
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if len(text.strip()) < 2 or text.strip().isdigit():
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return text
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try:
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# Build system prompt with custom context if provided
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base_prompt = f"""You are a professional translator. Your ONLY task is to translate text to {target_language}.
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CRITICAL RULES:
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1. Output ONLY the translated text - no explanations, no comments, no notes
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2. Preserve the exact formatting (line breaks, spacing, punctuation)
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3. Do NOT add any prefixes like "Here's the translation:" or "Translation:"
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4. Do NOT refuse to translate or ask clarifying questions
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5. If the text is already in {target_language}, return it unchanged
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6. Translate everything literally and accurately
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7. NEVER provide comments, opinions, or explanations - you are JUST a translator
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8. If you have any doubt about the translation, return the original text unchanged
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9. Do not interpret or analyze the content - simply translate word by word
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10. Your response must contain ONLY the translated text, nothing else"""
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if self.custom_system_prompt:
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system_content = f"""{base_prompt}
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ADDITIONAL CONTEXT AND INSTRUCTIONS:
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{self.custom_system_prompt}"""
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else:
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system_content = base_prompt
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# Use /api/chat endpoint (more compatible with all models)
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response = requests.post(
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f"{self.base_url}/api/chat",
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json={
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"model": self.model,
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"messages": [
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{
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"role": "system",
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"content": system_content
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},
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{
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"role": "user",
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"content": text
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}
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],
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"stream": False,
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"options": {
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"temperature": 0.3,
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"num_predict": 500
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}
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},
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timeout=120 # 2 minutes timeout
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)
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response.raise_for_status()
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result = response.json()
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translated = result.get("message", {}).get("content", "").strip()
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return translated if translated else text
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except requests.exceptions.ConnectionError:
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print(f"Ollama error: Cannot connect to {self.base_url}. Is Ollama running?")
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return text
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except requests.exceptions.Timeout:
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print(f"Ollama error: Request timeout after 120s")
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return text
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except Exception as e:
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print(f"Ollama translation error: {e}")
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return text
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def translate_image(self, image_path: str, target_language: str) -> str:
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"""Translate text within an image using Ollama vision model"""
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import base64
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try:
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# Read and encode image
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with open(image_path, 'rb') as img_file:
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image_data = base64.b64encode(img_file.read()).decode('utf-8')
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# Use /api/chat for vision models too
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response = requests.post(
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f"{self.base_url}/api/chat",
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json={
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"model": self.vision_model,
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"messages": [
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{
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"role": "user",
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"content": f"Extract all text from this image and translate it to {target_language}. Return ONLY the translated text, preserving the structure and formatting.",
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"images": [image_data]
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}
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],
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"stream": False
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},
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timeout=60
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)
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response.raise_for_status()
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result = response.json()
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return result.get("message", {}).get("content", "").strip()
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except Exception as e:
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print(f"Ollama vision translation error: {e}")
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return ""
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@staticmethod
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def list_models(base_url: str = "http://localhost:11434") -> List[str]:
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"""List available Ollama models"""
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try:
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response = requests.get(f"{base_url.rstrip('/')}/api/tags", timeout=5)
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response.raise_for_status()
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models = response.json().get("models", [])
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return [model["name"] for model in models]
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except Exception as e:
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print(f"Error listing Ollama models: {e}")
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return []
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class WebLLMTranslationProvider(TranslationProvider):
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"""WebLLM browser-based translation (client-side processing)"""
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def translate(self, text: str, target_language: str, source_language: str = 'auto') -> str:
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# WebLLM translation happens client-side in the browser
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# This is just a placeholder - actual translation is done by JavaScript
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# For server-side, we'll just pass through for now
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return text
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class OpenAITranslationProvider(TranslationProvider):
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"""OpenAI GPT translation implementation with vision support"""
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def __init__(self, api_key: str, model: str = "gpt-4o-mini", system_prompt: str = ""):
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self.api_key = api_key
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self.model = model
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self.custom_system_prompt = system_prompt
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def translate(self, text: str, target_language: str, source_language: str = 'auto') -> str:
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if not text or not text.strip():
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return text
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|
# Skip very short text or numbers only
|
|
if len(text.strip()) < 2 or text.strip().isdigit():
|
|
return text
|
|
|
|
try:
|
|
import openai
|
|
client = openai.OpenAI(api_key=self.api_key)
|
|
|
|
# Build system prompt with custom context if provided
|
|
base_prompt = f"""You are a professional translator. Your ONLY task is to translate text to {target_language}.
|
|
|
|
CRITICAL RULES:
|
|
1. Output ONLY the translated text - no explanations, no comments, no notes
|
|
2. Preserve the exact formatting (line breaks, spacing, punctuation)
|
|
3. Do NOT add any prefixes like "Here's the translation:" or "Translation:"
|
|
4. Do NOT refuse to translate or ask clarifying questions
|
|
5. If the text is already in {target_language}, return it unchanged
|
|
6. Translate everything literally and accurately
|
|
7. NEVER provide comments, opinions, or explanations - you are JUST a translator
|
|
8. If you have any doubt about the translation, return the original text unchanged
|
|
9. Do not interpret or analyze the content - simply translate word by word
|
|
10. Your response must contain ONLY the translated text, nothing else"""
|
|
|
|
if self.custom_system_prompt:
|
|
system_content = f"""{base_prompt}
|
|
|
|
ADDITIONAL CONTEXT AND INSTRUCTIONS:
|
|
{self.custom_system_prompt}"""
|
|
else:
|
|
system_content = base_prompt
|
|
|
|
response = client.chat.completions.create(
|
|
model=self.model,
|
|
messages=[
|
|
{"role": "system", "content": system_content},
|
|
{"role": "user", "content": text}
|
|
],
|
|
temperature=0.3,
|
|
max_tokens=500
|
|
)
|
|
|
|
translated = response.choices[0].message.content.strip()
|
|
return translated if translated else text
|
|
except Exception as e:
|
|
print(f"OpenAI translation error: {e}")
|
|
return text
|
|
|
|
def translate_image(self, image_path: str, target_language: str) -> str:
|
|
"""Translate text within an image using OpenAI vision model"""
|
|
import base64
|
|
|
|
try:
|
|
import openai
|
|
client = openai.OpenAI(api_key=self.api_key)
|
|
|
|
# Read and encode image
|
|
with open(image_path, 'rb') as img_file:
|
|
image_data = base64.b64encode(img_file.read()).decode('utf-8')
|
|
|
|
# Determine image type from extension
|
|
ext = image_path.lower().split('.')[-1]
|
|
media_type = f"image/{ext}" if ext in ['png', 'jpg', 'jpeg', 'gif', 'webp'] else "image/png"
|
|
|
|
response = client.chat.completions.create(
|
|
model=self.model, # gpt-4o and gpt-4o-mini support vision
|
|
messages=[
|
|
{
|
|
"role": "user",
|
|
"content": [
|
|
{
|
|
"type": "text",
|
|
"text": f"Extract all text from this image and translate it to {target_language}. Return ONLY the translated text, preserving the structure and formatting."
|
|
},
|
|
{
|
|
"type": "image_url",
|
|
"image_url": {
|
|
"url": f"data:{media_type};base64,{image_data}"
|
|
}
|
|
}
|
|
]
|
|
}
|
|
],
|
|
max_tokens=1000
|
|
)
|
|
|
|
return response.choices[0].message.content.strip()
|
|
except Exception as e:
|
|
print(f"OpenAI vision translation error: {e}")
|
|
return ""
|
|
|
|
|
|
class TranslationService:
|
|
"""Main translation service that delegates to the configured provider"""
|
|
|
|
def __init__(self, provider: Optional[TranslationProvider] = None):
|
|
if provider:
|
|
self.provider = provider
|
|
else:
|
|
# Auto-select provider based on configuration
|
|
self.provider = self._get_default_provider()
|
|
self.translate_images = False # Flag to enable image translation
|
|
|
|
def _get_default_provider(self) -> TranslationProvider:
|
|
"""Get the default translation provider from configuration"""
|
|
# Always use Google Translate by default to avoid API key issues
|
|
# Provider will be overridden per request in the API endpoint
|
|
return GoogleTranslationProvider()
|
|
|
|
def translate_text(self, text: str, target_language: str, source_language: str = 'auto') -> str:
|
|
"""
|
|
Translate a single text string
|
|
|
|
Args:
|
|
text: Text to translate
|
|
target_language: Target language code (e.g., 'es', 'fr', 'de')
|
|
source_language: Source language code (default: 'auto' for auto-detection)
|
|
|
|
Returns:
|
|
Translated text
|
|
"""
|
|
if not text or not text.strip():
|
|
return text
|
|
|
|
return self.provider.translate(text, target_language, source_language)
|
|
|
|
def translate_image(self, image_path: str, target_language: str) -> str:
|
|
"""
|
|
Translate text in an image using vision model (Ollama or OpenAI)
|
|
|
|
Args:
|
|
image_path: Path to image file
|
|
target_language: Target language code
|
|
|
|
Returns:
|
|
Translated text from image
|
|
"""
|
|
if not self.translate_images:
|
|
return ""
|
|
|
|
# Ollama and OpenAI support image translation
|
|
if isinstance(self.provider, OllamaTranslationProvider):
|
|
return self.provider.translate_image(image_path, target_language)
|
|
elif isinstance(self.provider, OpenAITranslationProvider):
|
|
return self.provider.translate_image(image_path, target_language)
|
|
|
|
return ""
|
|
|
|
def translate_batch(self, texts: list[str], target_language: str, source_language: str = 'auto') -> list[str]:
|
|
"""
|
|
Translate multiple text strings efficiently using batch processing.
|
|
|
|
Args:
|
|
texts: List of texts to translate
|
|
target_language: Target language code
|
|
source_language: Source language code (default: 'auto')
|
|
|
|
Returns:
|
|
List of translated texts
|
|
"""
|
|
if not texts:
|
|
return []
|
|
|
|
# Use provider's batch method if available
|
|
if hasattr(self.provider, 'translate_batch'):
|
|
return self.provider.translate_batch(texts, target_language, source_language)
|
|
|
|
# Fallback to individual translations
|
|
return [self.translate_text(text, target_language, source_language) for text in texts]
|
|
|
|
|
|
# Global translation service instance
|
|
translation_service = TranslationService()
|